---
title: "NEW YORK CITY: COVID-19 OVERALL VACCINATION TRENDS"
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    vertical_layout: fill
    source: embed
runtime: shiny
---

```{r setup, include=FALSE}
library(flexdashboard)
library(tidyverse)
library(plotly)
library(shiny)
library(ggplot2)
library(lubridate)



dose_data = 
  read_csv("./Data/doses-by-day.csv") %>%
  janitor::clean_names() %>% 
  mutate(date = as.Date(date))

health_data = 
  read_csv("./Data/New_York_State_Statewide_COVID-19_Reinfection_Data.csv") %>%
  janitor::clean_names() %>% 
  filter(redc_region == "New York City") %>% 
  mutate(begin_date_of_the_week = as.Date(begin_date_of_the_week, format = "%m/%d/%Y")) %>% 
  rename(date = begin_date_of_the_week) %>% 
  arrange(date)

death_hosp_data = 
  read_csv("./Data/hospitalizations_deaths.csv") %>% 
  janitor::clean_names() %>% 
  select(date_of_interest, hospitalized_count, death_count) %>% 
  mutate(date_of_interest = as.Date(date_of_interest, format = "%m/%d/%Y")) %>% 
  rename(date = date_of_interest) %>% 
  mutate(week = floor_date(date, "weeks", week_start = 1)) %>% 
  group_by(week) %>% 
  summarise(deaths = sum(death_count),
            hospitalizations = sum(hospitalized_count))

combined_data = 
  left_join(dose_data, health_data, by = "date") %>% 
  mutate(week = floor_date(date, "weeks", week_start = 1),
         primary_daily = admin_dose1_daily + admin_dose2_daily + admin_single_daily) %>% 
  group_by(week) %>% 
  summarise(All_Primary_Doses = sum(primary_daily),
            Dose_1 = sum(admin_dose1_daily),
            Dose_2 = sum(admin_dose2_daily),
            Single_Primary = sum(admin_single_daily),
            All_Doses = sum(admin_alldoses_daily),
            All_Boosters = sum(admin_additional_daily),
            Booster1_Additional = sum(admin_additional1_daily),
            Booster2_Additional = sum(admin_additional2_daily),
            First_Infections = sum(first_infections_count, na.rm = TRUE),
            Reinfections = sum(reinfections_count, na.rm = TRUE)) %>% 
  rename(date = week)



```


Column {.sidebar}
-----------------------------------------------------------------------

```{r}
vac_type = c("All_Doses", "All_Primary_Doses", "Dose_1", "Dose_2", "Single_Primary", "All_Boosters", "Booster1_Additional", "Booster2_Additional")


selectInput(
  inputId = "vac_type",
  label = h3("Vaccine Type"),
  choices = vac_type,
  selected = "All_Doses"
)


dateRangeInput(
  inputId = "date_range",
  label = h3("Date Range"),
  start = "2020-02-24",
  end = "2022-11-07",
  min = "2020-02-24",
  max = "2022-11-07",
  format = "yyyy-mm-dd"
)

```

Row {data-height=650}
-----------------------------------------------------------------------

### **Weekly Counts: Vaccines By Type, First Infections, and Re-Infections**

```{r}

renderPlotly({
  
  combined_data %>% 
    filter(
      date >= input$date_range[1],
      date <= input$date_range[2]) %>% 
  plot_ly(x = ~date, y = ~get(input$vac_type), type = "scatter", mode = "lines", name = "Vaccine Counts") %>%
    add_trace(x = ~date, y = ~First_Infections, type = "scatter", mode = "lines+markers", 
              name = "First Infections", line = list(color = "#d62728")) %>% 
    add_trace(x = ~date, y = ~Reinfections, type = "scatter", mode = "lines+markers", 
              name = "Re-Infections", line = list(color = "#e377c2")) %>% 
    layout(xaxis = list(title = "<b>Date<b>"),
           yaxis = list(title = "<b>Weekly Counts<b>"),
           legend = list(orientation = "h", xanchor = "center", x = 0.5, y = 5))
  
  })


```


### **Cumulative Counts: Primary and Booster Vaccines**

```{r}

renderPlotly({
  
  total_df = 
  dose_data %>%
  relocate(date, admin_additional1_cumulative, admin_additional2_cumulative, admin_alldoses_cumulative, admin_alldoses_primary, everything()) %>% 
  pivot_longer(
    admin_additional1_cumulative:admin_alldoses_primary,
    names_to = "cumulative",
    names_prefix = "admin_",
    values_to = "count") %>% 
  select(date, cumulative, count) %>% 
  arrange(date)

total_plot = 
  total_df %>%
  mutate(cumulative = case_when(cumulative == "alldoses_primary" ~ "All Primary Doses",
                                cumulative == "alldoses_cumulative" ~ "All Doses",
                                cumulative == "additional1_cumulative" ~ "1st Boosters + Additional",
                                cumulative == "additional2_cumulative" ~ "2nd Boosters + Additional")) %>% 
  filter(
    date >= input$date_range[1],
    date <= input$date_range[2]) %>%
  plot_ly(x = ~date, y = ~count) %>%
  add_lines(color = ~cumulative) %>% 
  layout(xaxis = list(title = "<b>Date<b>"),
         yaxis = list(title = "<b>Cumulative Counts<b>"),
         legend = list(orientation = "h", xanchor = "center", x = 0.5, y = 5))

total_plot
  
})

```

Row {data-height=350}
-------------------------------------

### **Weekly Counts: Vaccines By Type, First Infections, and Re-Infections**

```{r}

renderPlotly({
  
  death_hosp_data %>% 
  plot_ly(x = ~week) %>%
    add_trace(y = ~deaths, type = "scatter", mode = "lines+markers", 
              name = "Death Counts", line = list(color = "#d62728")) %>% 
    add_trace(y = ~hospitalizations, type = "scatter", mode = "lines+markers", 
              name = "Hospitalization Counts", line = list(color = "#e377c2")) %>% 
    layout(xaxis = list(title = "<b>Date<b>"),
           yaxis = list(title = "<b>Weekly Counts<b>"),
           legend = list(orientation = "h", xanchor = "center", x = 0.5))
  
  })


```